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on optical transmission through falling snow and the models we have are inaccurate. By conducting experiments in Arctic weather over a longer period of time, at different geographic locations, we hope to build
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bring together different disciplines and perspectives to foster the sustainable management of mountain forests in the Alps. The current position will be part of the Center for Forest Management in the Alps, and
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-scale and reliable use of AI across different industries. Your work assignments You will work at the intersection of machine learning, cybersecurity, and privacy, developing methods to make AI systems
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intelligence, machine learning, statistical estimation methods, software tools, and big-data frameworks. Programming languages such as e.g. Python, C++, and LABVIEW. Emission control rules and regulations in
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: electronic structure calculations (plane wave DFT if possible), statistical thermodynamics, molecular dynamics. Skills in Python, bash scripting, Fortran 90 and machine-learning would be appreciated. The PIIM
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. Experience in programming (R and/or Python) Strong interest for societal problems and in particular fairness and ethics. Strong passion for real-world problems faced by different types of organizations
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where we shape a sustainable future together, we wish to make a difference. Do you want to be involved and contribute to our development? Together, we can create a sustainable future through knowledge and
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electron microscopy (TEM). Interest and experience in working with industrial problems. Experience/ knowledge within electrochemistry. Experience with programming and/or data processing in Python. Good oral
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sustainable process. Assess the techno-economic impact of different alternative processes, from the traditional coal-based blast furnace to green steel production. Evaluate electrification and heat integration
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of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e.g. random forest (RF